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SASInstitute A00-255 (SAS Predictive Modeling Using SAS Enterprise Miner 14) exam is designed for professionals who want to demonstrate their abilities in predictive modeling using SAS Enterprise Miner 14. A00-255 exam measures the candidate's ability to use SAS Enterprise Miner to perform data exploration, variable selection, data preparation, and model assessment. Additionally, the exam tests the candidate's knowledge of SAS programming techniques and statistical analyses.

Passing the SASInstitute A00-255 Certification Exam demonstrates that an individual has the skills and knowledge required to use SAS Enterprise Miner to build and deploy predictive models. SAS Predictive Modeling Using SAS Enterprise Miner 14 certification can enhance an individual's career prospects and open up new job opportunities in industries such as finance, healthcare, and marketing.

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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q21-Q26):

NEW QUESTION # 21
How many unique indicator variables were created during imputation?
Response:

A. 11-20
B. 1-10
C. 21 or higher
D. none

Answer: A

NEW QUESTION # 22
Perform these tasks in SAS Enterprise Miner:
- Add a Decision Tree node after the Impute node with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the decision tree to use 1 for Number of Surrogate Rules and Largest for Method in Subtree. Do not change any other property of the Decision Tree node.
- Add another Neural Network node after the decision tree with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the Neural Network model to use Average Error for Model Selection Criterion. Do not change any other property for the Neural Network node. Run the process flow.
How many leaves are there in the decision tree?
Response:

A. 11-15
B. 1-10
C. 21 or more
D. 16-20

Answer: A

NEW QUESTION # 23
Which of the following is not true about results produced by the Regression node?
Response:

A. Model Information provides you with information that includes the number of target categories and the number of model parameters.
B. Type 3 Analysis of Effects provides you with information about the number of parameters that each input contributes to the model.
C. Variable Summary information identifies the roles of variables used by the Regression node.
D. Fit Statistics can provide information that affects decision predictions, but does not affect estimate predictions.

Answer: D

NEW QUESTION # 24
An analyst is performing a market basket analysis (affinity analysis) on the purchase of Shaving Cream and Seltzer Water. The purchase data from a set of 250 customers is shown below:

What is the confidence of the rule "Shaving Cream implies Seltzer Water"? You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:

A. 57%
B. 60%
C. 67%
D. 40%

Answer: A

NEW QUESTION # 25
Which of the following is not a good reason to"regularize" input distributions using a simple transformation?
Response:

A. When you perform regression, inputs with highly skewed or highly kurtotic distributions can be selected over inputs that would yield better overall predictions.
B. Another benefit is ease in model interpretation.
C. Regression models are sensitive to extreme or outlying values in the input space.
D. One benefit is improved model performance.

Answer: B

NEW QUESTION # 26
......

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